Utilizing Head Rotation Data in DNN-based Multi-Channel Speech Enhancement for Hearing AIDS

When multiple microphones are available, speech enhancement approaches can utilize spatial information to improve speech perception for hearing aid users. In this study, we optimize and extend an existing DNN-based speech enhancement approach to investigate the utility of head rotation (HR) information in several ways. First, optimization of the DNN indicates that especially input features with inter-channel phase and inter-channel level difference information enable improved processing of dynamic spatial cues. Second, introducing encoded HR data as an additional input to the DNN model leads to notable enhancements, particularly when target direction is included. Nevertheless, perceptible improvements are still observed when relative HR data is utilized, that is without information on the absolute target direction. Given that relative HR data can be accurately captured using angular acceleration sensors and integrated with minimal algorithmic complexity, leveraging such data remains an important objective in improving speech enhancement algorithms.